Book Image

Apache Spark Machine Learning Blueprints

By : Alex Liu
Book Image

Apache Spark Machine Learning Blueprints

By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (18 chapters)
Apache Spark Machine Learning Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 1. Spark for Machine Learning

This chapter provides an introduction to Apache Spark from a Machine Learning (ML) and data analytics perspective, and also discusses machine learning in relation to Spark computing. Here, we first present an overview of Apache Spark, as well as Spark's advantages for data analytics, in comparison to MapReduce and other computing platforms. Then we discuss five main issues, as below:

  • Machine learning algorithms and libraries

  • Spark RDD and dataframes

  • Machine learning frameworks

  • Spark pipelines

  • Spark notebooks

All of the above are the most important topics that any data scientist or machine learning professional is expected to master, in order to fully take advantage of Apache Spark computing. Specifically, this chapter will cover all of the following six topics.

  • Spark overview and Spark advantages

  • ML algorithms and ML libraries for Spark

  • Spark RDD and dataframes

  • ML Frameworks, RM4Es and Spark computing

  • ML workflows and Spark pipelines

  • Spark notebooks introduction